## Calculating Grades with Pandas DataFrames

Are you tired of calculating grades by hand? Do you want a quicker and more efficient way to determine your students’ final marks?

Look no further than Pandas DataFrames. With a few lines of code, you can easily calculate the total score, homework scores, quiz scores, and letter grade for your students.

## Exam Total Score

To begin, let’s look at how to calculate the exam total score. First, you will need to know the weights of each exam.

For example, Exam 1 might be worth 20% of the final grade, Exam 2 might be worth 30%, and Exam 3 might be worth 50%. Next, use a for loop to iterate through the exams and calculate the raw score.

This can be done by dividing the student’s score by the maximum points possible and multiplying it by the weight of the exam. Finally, sum up all the raw scores to get the total exam score.

## Homework Scores

### Collecting Homework Data

The first step in calculating homework scores is to collect the homework data. To do this, use the DataFrame.filter() method with a regex to select the columns that contain the homework scores.

For example, if your homework scores are labelled as “Homework 1”, “Homework 2”, etc., you can use the following code:

`homework_scores = df.filter(regex='Homework')`

Next, collect the maximum points possible for each homework assignment. Create another DataFrame with the same shape as the homework_scores DataFrame, but with the maximum points as the values.

### For example:

`homework_max_points = pd.DataFrame(np.ones(homework_scores.shape) * 10, columns=homework_scores.columns)`

This assumes that each homework assignment is worth 10 points. Adjust the code accordingly based on your grading system.

### Total Score Calculation

Once you have the homework scores and maximum points, you can calculate the total score for each student. Use the sum() method with axis=1 to sum the scores across each row (i.e. each student).

This will give you the total homework score for each student. “`

`total_homework_score = homework_scores.sum(axis=1)`

To make sure that the total homework score is represented as a ratio of the maximum points possible, divide the total score by the maximum points possible and multiply by 100.

`total_homework_ratio = total_homework_score / (homework_max_points.sum(axis=1)) * 100`

This will give you a percentage score for each student’s homework.

### Average Score Calculation

To calculate the average score for each homework assignment, divide the total score by the number of homework assignments. You can obtain the number of homework assignments using shape[1], which returns the number of columns in the DataFrame.

`average_homework_score = homework_scores.sum(axis=0) / homework_scores.shape[1]`

This will give you the average score for each homework assignment. To represent the average score as a ratio of the maximum points possible, divide the average score by the maximum points possible and multiply by 100.

`average_homework_ratio = average_homework_score / homework_max_points.max() * 100`

This will give you a percentage score for the average score of each homework assignment.

### Maximum Homework Score

To find the maximum homework score, use the max() method on the homework_scores DataFrame. “`

`max_homework_score = homework_scores.max()`

This will give you the maximum homework score for each homework assignment.

## Quiz Score

### Collecting Quiz Data

To calculate the quiz scores, follow a similar process to the homework scores. Use the DataFrame.filter() method with a regex to select the columns that contain the quiz scores.

For example, if your quiz scores are labelled as “Quiz 1”, “Quiz 2”, etc., you can use the following code:

`quiz_scores = df.filter(regex='Quiz')`

Next, collect the maximum points possible for each quiz. Create another DataFrame with the same shape as the quiz_scores DataFrame, but with the maximum points as the values.

### For example:

`quiz_max_points = pd.DataFrame(np.ones(quiz_scores.shape) * 20, columns=quiz_scores.columns)`

This assumes that each quiz is worth 20 points. Adjust the code accordingly based on your grading system.

### Total Score Calculation

Once you have the quiz scores and maximum points, you can calculate the total score for each student. Use the sum() method with axis=1 to sum the scores across each row (i.e. each student).

This will give you the total quiz score for each student. “`

`total_quiz_score = quiz_scores.sum(axis=1)`

To make sure that the total quiz score is represented as a ratio of the maximum points possible, divide the total score by the maximum points possible and multiply by 100.

`total_quiz_ratio = total_quiz_score / (quiz_max_points.sum(axis=1)) * 100`

This will give you a percentage score for each student’s quizzes.

### Average Score Calculation

To calculate the average score for each quiz, divide the total score by the number of quizzes. You can obtain the number of quizzes using shape[1], which returns the number of columns in the DataFrame.

`average_quiz_score = quiz_scores.sum(axis=0) / quiz_scores.shape[1]`

This will give you the average score for each quiz. To represent the average score as a ratio of the maximum points possible, divide the average score by the maximum points possible and multiply by 100.

`average_quiz_ratio = average_quiz_score / quiz_max_points.max() * 100`

This will give you a percentage score for the average score of each quiz. Maximum

### Quiz Score

To find the maximum quiz score, use the max() method on the quiz_scores DataFrame.

`max_quiz_score = quiz_scores.max()`

This will give you the maximum quiz score for each quiz. In conclusion, calculating grades with Pandas DataFrames can save time and make the process more efficient.

Collecting homework and quiz data, calculating the total score, average score, and maximum score for each, ensures that each student’s grade will accurately represent their performance.

## Letter Grade

Now that we have calculated the raw scores for each student, it’s time to determine their letter grades. To do this, we need to define the weightings of each component (exams, quizzes, and homework).

Once we have these weightings, we can calculate the final score for each student and map it to a letter grade.

### Weightings

Assuming that exams are worth 60% of the final grade, quizzes are worth 20%, and homework is worth 20%, we can create a Series to store the weightings. For example:

`weights = pd.Series({'Exam 1 Score': 0.2, 'Exam 2 Score': 0.3, 'Exam 3 Score': 0.5, 'Quiz Score': 0.2, 'Homework Score': 0.2})`

This Series assigns the weightings to each component.

Note that the weightings must add up to 1.

### Final Score Calculation

To calculate the final score for each student, let’s first select the columns that we need. We can do this using the DataFrame.select() method:

`selected = df.select(['Exam 1 Score', 'Exam 2 Score', 'Exam 3 Score', 'Quiz Score', 'Homework Score'])`

Next, we need to multiply each selected column by its corresponding weighting.

We can do this by using the DataFrame.multiply() method with the weights Series:

`weighted = selected.multiply(weights)`

Finally, we can sum up the weighted columns to get the final score for each student:

`final_score = weighted.sum(axis=1)`

To make sure that the final score is represented as a ratio of the maximum points possible, divide the final score by the maximum points possible and multiply by 100. “`

`final_score_ratio = final_score / (weights.sum() * 100)`

This will give you a percentage score for the final grade of each student.

### Grade Mapping

Now that we have the final score for each student, we need to map it to a letter grade. One way to do this is by creating a dictionary that maps score ranges to letter grades.

### For example:

`grade_map = { 90: 'A', 80: 'B', 70: 'C', 60: 'D', 0: 'F'}`

This dictionary maps scores from 90-100 to an ‘A’, scores from 80-89 to a ‘B’, and so on. To ensure that students who are close to a higher grade are not “penalized” for being on the lower end of that grade range (i.e. a student with a 79 getting a B- instead of a C+), we can use the ceil() function from the math library to round scores up to the nearest multiple of 10:

```
import math
final_score_rounded = final_score.apply(lambda x: math.ceil(x / 10) * 10)
```

This rounds up the final score for each student to the nearest multiple of 10. For example, a final score of 78 would be rounded up to 80.

Next, we can map the final score to a letter grade using the dictionary and the Series.map() method:

`final_grade = final_score_rounded.map(grade_map)`

This will give you a Series with the letter grade for each student.

### Categorical Data

Lastly, let’s convert the letter grades to categorical data. This will make it easier to analyze and visualize the grade distribution.

To do this, we need to specify the categories of the data (in this case, the letter grades) and whether they are ordered (in this case, they are ordered from highest to lowest). “`

```
categories = ['A', 'B', 'C', 'D', 'F']
ordered = True
final_grade_categorical = pd.Categorical(final_grade, categories=categories, ordered=ordered)
```

This will give you a Categorical object with the letter grades for each student.

You can assign this to a new column in the DataFrame using the DataFrame.assign() method:

`df = df.assign(Final Grade=final_grade_categorical)`

In conclusion, using a combination of weightings, final score calculation, grade mapping, and categorical data conversion, we can accurately determine each student’s letter grade. This makes it easy to analyze and visualize the grade distribution of the class and identify any areas of improvement.

## Exam Data Sample

### Here is a sample of what the exam data might look like in a table:

NetID | Exam 1 Score | Exam 2 Score | Exam 3 Score |
---|---|---|---|

jdoe01 | 85 | 92 | 78 |

asmi01 | 91 | 88 | 95 |

jlee01 | 79 | 85 | 90 |

kwoo01 | 93 | 96 | 91 |

As you can see, each row represents a student, and each column represents an exam. You can use this table to calculate the raw scores for each student and sum them up to get the total exam score.

In this article, we discussed how to use Pandas DataFrames to calculate grades efficiently. We focused on calculating the total score, homework scores, quiz scores, and letter grade for each student.

We explored topics such as looping through exams, collecting homework and quiz data, calculating average and maximum scores, and mapping final scores to letter grades. By using weightings and mapping scores to letter grades, we are able to accurately reflect each student’s performance and identify areas of improvement.

The takeaway from this article is that using Pandas DataFrames to calculate grades can save time and provide an efficient way to analyze performance.